AstroGenesis Multi-Agent Framework Integrates Literature, Data and Surrogate Models for Blazar Research

The framework deploys specialized agents for retrieval-augmented generation, observational data pipelines, and theoretical modeling via pretrained neural surrogates that replace expensive radiative-transfer calculations. Evaluation used two custom benchmarks: 76.6 percent of single-paper questions and 79.2 percent of multi-paper questions returned at least one relevant publication in the top five ranked results. Natural-language queries trigger traceable workflows that combine these components without manual scripting.

Existing coverage emphasizes the prototype but understates the architectural choice to embed domain-specific surrogate models rather than generic large language models for physical consistency. This design reduces hallucinated spectra yet ties performance to the quality and coverage of the training simulations, a constraint not quantified in the current benchmarks. Extension beyond blazars will require new surrogate training sets and domain-adapted retrieval corpora.

The work connects to broader trends in agentic scientific AI seen in chemistry and materials but remains the first explicit multi-agent system aimed at multimessenger astrophysics. Reproducibility gains come from the built-in citation and data provenance chains, yet the absence of an open benchmark suite or third-party replication leaves claims of workflow acceleration untested at scale.

Next steps include public release of the agent codebase and expansion to gamma-ray burst and neutrino-source domains, which would test whether the current retrieval-ranking pipeline generalizes without retraining.